Journal of Biomedicine and Biotechnology
Volume 2009 (2009), Article ID 632786, 7 pages
doi:10.1155/2009/632786
Research Article
Developing Prognostic Systems of Cancer Patients by Ensemble Clustering
1Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
2Department of Computer Science, The George Washington University, Washington DC 20052, USA
3The George Washington University Cancer Institute, The George Washington University, Washington DC 20037, USA
4Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA
5Department of Pathology, The George Washington University Medical Center, Washington DC 20037, USA
Received 7 January 2009; Accepted 27 March 2009
Academic Editor: Zhenqiu Liu
Copyright © 2009 Dechang Chen et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- SEER, http://seer.cancer.gov/.
- NCDB, http://www.facs.org/cancer/ncdb/index.html.
- F. L. Greene, C. C. Compton, A. G. Fritz, J. P. Shah, and D. P. Winchester, Eds., AJCC Cancer Staging Atlas, F. L. Greene, C. C. Compton, A. G. Fritz, J. P. Shah, and D. P. Winchester, Eds., Springer, New York, NY, USA, 2006.
- D. Chen, K. Xing, D. Henson, and L. Sheng, “Group testing in the development of an expanded cancer staging system,” in Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA '08), pp. 589–594, San Diego, Calif, USA, December 2008. View at Publisher · View at Google Scholar
- D. Chen, K. Xing, D. Henson, L. Sheng, A. M. Schwartz, and X. Cheng, “A clustering-based approach to predict outcome in cancer patients,” to appear in International Journal of Data Mining and Bioinformatics.
- K. Xing, D. Chen, D. Henson, and L. Sheng, “A clustering-based approach to predict outcome in cancer patients,” in Proceedings of the 6th International Conference on Machine Learning and Applications (ICMLA '07), pp. 541–546, Cincinnati, Ohio, USA, December 2007. View at Publisher · View at Google Scholar
- L. Kaufman and P. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, NY, USA, 1990.
- T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, NY, USA, 2001.
- J. P. Klein and M. L. Moeschberger, Survival Analysis: Techniques for Censored and Truncated Data, Springer, New York, NY, USA, 2nd edition, 2003.
- E. L. Kaplan and P. Meier, “Nonparametric estimation from incomplete observations,” Journal of the American Statistical Association, vol. 53, no. 282, pp. 457–481, 1958. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
- E. A. Gehan, “A generalized Wilcoxon test for comparing arbitrarily singly-censored samples,” Biometrika, vol. 52, pp. 203–223, 1965.
- N. Breslow, “A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship,” Biometrika, vol. 57, no. 3, pp. 579–594, 1970. View at Publisher · View at Google Scholar
- R. E. Tarone and J. Ware, “On distribution free tests for equality of survival distributions,” Biometrika, vol. 64, no. 1, pp. 156–160, 1977. View at Publisher · View at Google Scholar
- A. L. N. Fred and A. K. Jain, “Data clustering using evidence accumulation,” in Proceedings of the 16th International Conference on Pattern Recognition (ICPR '02), vol. 4, pp. 276–280, Quebec, Canada, August 2002. View at Publisher · View at Google Scholar
- D. Chen, Z. Zhang, Z. Liu, and X. Cheng, “An ensemble method of discovering sample classes using gene expression profiling,” in Data Mining in Biomedicine, P. M. Pardalos, V. L. Boginski, and A. Vazacopoulos, Eds., vol. 7 of Springer Optimization and Its Applications, pp. 39–46, Springer, New York, NY, USA, 2007. View at Publisher · View at Google Scholar